Codebook embedding generation and processing

ABSTRACT

Aspects of the present disclosure relate to wireless communication systems, and in particular, to techniques for generation and processing of an embedding representing a beam for communication. Certain aspects provide a method for wireless communication by a wireless node. The method generally includes receiving an embedding representing a characterization associated with a beam; providing the embedding to a machine learning (ML) model; generating one or more communication parameters for communication using the beam via the ML model based on the embedding; and communicating using the one or more communication parameters.

INTRODUCTION

Aspects of the present disclosure relate to wireless communicationsystems, and in particular, to techniques for generation and processingof an embedding representing a beam for communication.

Communications using “millimeter wave” (“mmW” or “mmWave”) or nearmmWave radio frequency band (e.g., 3 GHz-300 GHz) may have higher pathloss and a shorter range compared to lower frequency communications.Accordingly, communications between a base station and a user equipment(UE) may use beamforming to improve path loss and range. To do so, thebase station and the UE may each include multiple antennas, such asantenna elements, antenna panels, and/or antenna arrays to facilitatethe beamforming.

A beam to be used for communication may be characterized using varioustechniques. One technique includes using an array gain sphere torepresent a beam. An array gain sphere provides detailed informationabout the beam, but may be challenging to use when performing beamprocessing. Therefore, what is needed are techniques for representing abeam using a low-dimensional representation of the beam to facilitateefficient beam processing.

BRIEF SUMMARY

Certain aspects provide a method for wireless communication by awireless node. The method generally includes receiving an embeddingrepresenting a characterization associated with a beam; providing theembedding to a machine learning (ML) model; generating one or morecommunication parameters for communication using the beam via the MLmodel based on the embedding; and communicating using the one or morecommunication parameters.

Certain aspects provide a method for wireless communication. The methodgenerally includes receiving a characterization associated with a beam;generating an embedding based on the characterization; and providing theembedding to a wireless node.

Other aspects provide processing systems configured to perform theaforementioned methods as well as those described herein;non-transitory, computer-readable media comprising instructions that,when executed by one or more processors of a processing system, causethe processing system to perform the aforementioned methods as well asthose described herein; a computer program product embodied on acomputer readable storage medium comprising code for performing theaforementioned methods as well as those further described herein; and aprocessing system comprising means for performing the aforementionedmethods as well as those further described herein.

The following description and the related drawings set forth in detailcertain illustrative features of one or more aspects.

BRIEF DESCRIPTION OF THE DRAWINGS

The appended figures depict some aspects of the present disclosure andare therefore not to be considered limiting of the scope of thisdisclosure.

FIG. 1 depicts an example of a wireless communications system 100, inwhich aspects described herein may be implemented.

FIG. 2 depicts aspects of an example base station (BS) and a userequipment (UE).

FIGS. 3A-3D depict aspects of data structures for a wirelesscommunication network.

FIG. 4 illustrates example array gain spheres.

FIG. 5 illustrates example operations for embedding generation andprocessing, in accordance with certain aspects of the presentdisclosure.

FIGS. 6, 7, and 8 illustrate example sampling techniques.

FIG. 9 illustrates an example technique for training an autoencoderusing a graphical convolution network (GCN), in accordance with certainaspects of the present disclosure.

FIG. 10 illustrates generation of an embedding from a discretizedsphere, in accordance with certain aspects of the present disclosure.

FIG. 11 illustrates a denoising autoencoder, in accordance with certainaspects of the present disclosure.

FIG. 12 illustrates techniques for generating an embedding using aFourier transform, in accordance with certain aspects of the presentdisclosure.

FIG. 13 illustrates example operations for beam rotation in accordancewith certain aspects of the present disclosure.

FIG. 14 illustrates example operations for beam comparison, inaccordance with certain aspects of the present disclosure.

FIG. 15 illustrates example operations for signal quality prediction, inaccordance with certain aspects of the present disclosure.

FIG. 16 is a flow diagram illustrating example operations for wirelesscommunication by a target entity, in accordance with certain aspects ofthe present disclosure.

FIG. 17 is a flow diagram illustrating example operations for wirelesscommunication by a network entity, in accordance with certain aspects ofthe present disclosure

FIGS. 18 and 19 illustrate example electronic devices, in accordancewith certain aspects of the present disclosure.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe drawings. It is contemplated that elements and features of oneembodiment may be beneficially incorporated in other embodiments withoutfurther recitation.

DETAILED DESCRIPTION

Aspects of the present disclosure provide apparatuses and techniques forgenerating and processing an embedding representing a beam forcommunication. In some aspects of the present disclosure, an embeddingrepresenting a characterization of a beam may be generated by an encodertrained using an autoencoder training procedure. Once trained, theencoder may generate values for a look-up table indicating embeddingsrepresenting various beams. In some aspects, the characterization of thebeam may be in the form of an array gain sphere. The array gain spheredata may be converted into a graph data structure, and the autoencodertraining procedure may involve using a graphical convolution network toprocess the graph and train the encoder for generation of the values forthe look-up table.

In some aspects, the look-up table may be provided to a user equipment(UE) for processing. For instance, a UE may retrieve, from the look-uptable, an embedding representing a beam of interest and input theembedding into a machine learning model to generate a communicationparameter to be used. As one example, the machine learning model mayperform beam rotation, predict signal quality parameters (e.g.,reference signal receive power (RSRP)) associated with the beam,calculate inter-beam similarities, performing mobility estimation, orperform codebook characterization, as described further herein.

Using an embedding as described herein results in reduced storage spaceand computation complexity at the UE by providing a low-dimensionalrepresentation of a beam that can be stored at the UE and used forperforming computations. Generation of the embedding enables variousevaluations (e.g., inter-beam similarity evaluation) from a singlemetric and provides a richer representation than conventionalimplementations that have codebook characterizations using descriptivestatistics.

Introduction to Wireless Communication Networks

FIG. 1 depicts an example of a wireless communications system 100, inwhich aspects described herein may be implemented.

Generally, wireless communications system 100 includes base stations(BSs) 102, user equipments (UEs) 104, one or more core networks, such asan Evolved Packet Core (EPC) 160 and 5G Core (5GC) network 190, whichinteroperate to provide wireless communications services.

Base stations 102 may provide an access point to the EPC 160 and/or 5GC190 for a user equipment 104, and may perform one or more of thefollowing functions: transfer of user data, radio channel ciphering anddeciphering, integrity protection, header compression, mobility controlfunctions (e.g., handover, dual connectivity), inter-cell interferencecoordination, connection setup and release, load balancing, distributionfor non-access stratum (NAS) messages, NAS node selection,synchronization, radio access network (RAN) sharing, multimediabroadcast multicast service (MBMS), subscriber and equipment trace, RANinformation management (RIM), paging, positioning, delivery of warningmessages, among other functions. Base stations may include and/or bereferred to as a gNB, NodeB, eNB, ng-eNB (e.g., an eNB that has beenenhanced to provide connection to both EPC 160 and 5GC 190), an accesspoint, a base transceiver station, a radio base station, a radiotransceiver, or a transceiver function, or a transmission receptionpoint in various contexts.

Base stations 102 wirelessly communicate with UEs 104 via communicationslinks 120. Each of base stations 102 may provide communication coveragefor a respective geographic coverage area 110, which may overlap in somecases. For example, small cell 102′ (e.g., a low-power base station) mayhave a coverage area 110′ that overlaps the coverage area 110 of one ormore macrocells (e.g., high-power base stations).

The communication links 120 between base stations 102 and UEs 104 mayinclude uplink (UL) (also referred to as reverse link) transmissionsfrom a user equipment 104 to a base station 102 and/or downlink (DL)(also referred to as forward link) transmissions from a base station 102to a user equipment 104. The communication links 120 may usemultiple-input and multiple-output (MIMO) antenna technology, includingspatial multiplexing, beamforming, and/or transmit diversity in variousaspects.

Examples of UEs 104 include a cellular phone, a smart phone, a sessioninitiation protocol (SIP) phone, a laptop, a personal digital assistant(PDA), a satellite radio, a global positioning system, a multimediadevice, a video device, a digital audio player, a camera, a gameconsole, a tablet, a smart device, a wearable device, a vehicle, anelectric meter, a gas pump, a large or small kitchen appliance, ahealthcare device, an implant, a sensor/actuator, a display, or othersimilar devices. Some of UEs 104 may be internet of things (IoT) devices(e.g., parking meter, gas pump, toaster, vehicles, heart monitor, orother IoT devices), always on (AON) devices, or edge processing devices.UEs 104 may also be referred to more generally as a station, a mobilestation, a subscriber station, a mobile unit, a subscriber unit, awireless unit, a remote unit, a mobile device, a wireless device, awireless communications device, a remote device, a mobile subscriberstation, an access terminal, a mobile terminal, a wireless terminal, aremote terminal, a handset, a user agent, a mobile client, or a client.

Wireless communication network 100 includes training component 199,which may be configured to process characterization of a beam togenerate an embedding. Wireless network 100 further includes beamprocessing component 198, which may be used configured to process anembedding using machine learning for communication.

FIG. 2 depicts aspects of an example base station (BS) 102 and a userequipment (UE) 104.

Generally, base station 102 includes various processors (e.g., 220, 230,238, and 240), antennas 234 a-t (collectively 234), transceivers 232 a-t(collectively 232), which include modulators and demodulators, and otheraspects, which enable wireless transmission of data (e.g., source data212) and wireless reception of data (e.g., data sink 239). For example,base station 102 may send and receive data between itself and userequipment 104.

Base station 102 includes controller/processor 240, which may beconfigured to implement various functions related to wirelesscommunications. In the depicted example, controller/processor 240includes training component 241, which may be representative of beamprocessing component 199 of FIG. 1 . Notably, while depicted as anaspect of controller/processor 240, training component 241 may beimplemented additionally or alternatively in various other aspects ofbase station 102 in other implementations.

Generally, user equipment 104 includes various processors (e.g., 258,264, 266, and 280), antennas 252 a-r (collectively 252), transceivers254 a-r (collectively 254), which include modulators and demodulators,and other aspects, which enable wireless transmission of data (e.g.,source data 262) and wireless reception of data (e.g., data sink 260).

User equipment 102 includes controller/processor 280, which may beconfigured to implement various functions related to wirelesscommunications. In the depicted example, controller/processor 280includes beam processing component 281, which may be representative ofbeam processing component 198 of FIG. 1 . Notably, while depicted as anaspect of controller/processor 280, beam processing component 281 may beimplemented additionally or alternatively in various other aspects ofuser equipment 104 in other implementations.

FIGS. 3A-3D depict aspects of data structures for a wirelesscommunication network, such as wireless communication network 100 ofFIG. 1 . In particular, FIG. 3A is a diagram 300 illustrating an exampleof a first subframe within a 5G (e.g., 5G NR) frame structure, FIG. 3Bis a diagram 330 illustrating an example of DL channels within a 5Gsubframe, FIG. 3C is a diagram 350 illustrating an example of a secondsubframe within a 5G frame structure, and FIG. 3D is a diagram 380illustrating an example of UL channels within a 5G subframe.

Further discussions regarding FIG. 1 , FIG. 2 , and FIGS. 3A-3D areprovided later in this disclosure.

Introduction to mmWave Wireless Communications

In wireless communications, an electromagnetic spectrum is oftensubdivided into various classes, bands, channels, or other features. Thesubdivision is often provided based on wavelength and frequency, wherefrequency may also be referred to as a carrier, a subcarrier, afrequency channel, a tone, or a subband.

5G networks may utilize several frequency ranges, which in some casesare defined by a standard, such as the 3GPP standards. For example, 3GPPtechnical standard TS 38.101 currently defines Frequency Range 1 (FR1)as including 600 MHz-6 GHz, though specific uplink and downlinkallocations may fall outside of this general range. Thus, FR1 is oftenreferred to (interchangeably) as a “Sub-6 GHz” band.

Similarly, TS 38.101 currently defines Frequency Range 2 (FR2) asincluding 26-41 GHz, though again specific uplink and downlinkallocations may fall outside of this general range. FR2, is sometimesreferred to (interchangeably) as a “millimeter wave” (“mmW” or “mmWave”)band, despite being different from the extremely high frequency (EHF)band (30 GHz-300 GHz) that is identified by the InternationalTelecommunications Union (ITU) as a “millimeter wave” band becausewavelengths at these frequencies are between 1 millimeter and 10millimeters.

Communications using mmWave/near mmWave radio frequency band (e.g., 3GHz-300 GHz) may have higher path loss and a shorter range compared tolower frequency communications. Accordingly, in FIG. 1 , mmWave basestation 180 may utilize beamforming 182 with the UE 104 to improve pathloss and range. To do so, base station 180 and the UE 104 may eachinclude a plurality of antennas, such as antenna elements, antennapanels, and/or antenna arrays to facilitate the beamforming.

In some cases, base station 180 may transmit a beamformed signal to UE104 in one or more transmit directions 182′. UE 104 may receive thebeamformed signal from the base station 180 in one or more receivedirections 182″. UE 104 may also transmit a beamformed signal to thebase station 180 in one or more transmit directions 182″. Base station180 may receive the beamformed signal from UE 104 in one or more receivedirections 182′. Base station 180 and UE 104 may then perform beamtraining to determine the best receive and transmit directions for eachof base station 180 and UE 104. Notably, the transmit and receivedirections for base station 180 may or may not be the same. Similarly,the transmit and receive directions for UE 104 may or may not be thesame.

Example of Array Gain Sphere Processing Using Machine Learning (ML)

Machine Learning (ML) based approaches are becoming an increasinglyattractive option for complex communication systems, includingmmWave-enabled communication systems. Machine learning is generally theprocess of producing a trained model (e.g., an artificial neuralnetwork, a tree, or other structures), which represents a generalizedfit to a set of training data that is known a priori. Applying thetrained model to new data produces inferences, which may be used to gaininsights into the new data. In some cases, applying the model to the newdata is described as “running an inference” on the new data.

Both ML and non-ML mmWave applications use information that describemmWave beams as an input to produce a target objective (e.g., schedulebeams based on UE mobility). A codebook characterization provides acollection of different measures to describe individual beams andinter-beam relations. This set of information is currently obtained byempirical study and simulation. However, codebook characterization isnot ideal, or practical, for consumption by downstream mmWaveapplications. Therefore, the performance of mmWave applications thatrely on codebook beam characterization may be limited by the currentrepresentation of beams. Certain aspects of the present disclosure aredirected to techniques for generating a low-dimension beamcharacterization (also referred to as “codebook embedding” or“embedding” for short), which can be used in arbitrary mmWaveapplications, codebook characterization, and beam analysis, to name afew examples.

A beam may be characterized using various techniques. For example, abeam may be represented by a beam ID, which is codebook specific.However, merely using a beam ID to characterize a beam may beinsufficient as a general tool usable with arbitrary codebooks.

In some cases, beams may be characterized by definingparent-child-neighbor relationships of beams, providing a coarsecharacterization of the beams. A parent-child relationship between twobeams may indicate a similarity in the features of the beams. However,using parent-child-neighbor relationships may be not provide a notion ofrelative closeness of beams.

Summary statistics of beams may be used for beam characterization.Summary statistics may be extracted from an array gain sphere which maybe used to provide complete information regarding the beam. Summarystatistics such as peak phi angle (φ), theta angle (θ), and gainassociated with the beam may result in two beams with very differentprofiles sharing a misleadingly similar representation.

FIG. 4 illustrates example array gain spheres for various beams. Arraygain spheres may be used to characterize multiple beams, such as beam₁to beam₅, as shown. The array gain sphere indicates the gain associatedwith a beam at each phi and theta angle. The array gain sphere for beam₁may have a peak center of mass at spherical coordinate 403 and the arraygain sphere for beam₅ may have a peak center of mass at sphericalcoordinate 407. For instance, the peak center of mass for beam₁ may beat theta angle 30, phi angle 179, and have a gain of −95 dB.

While an array gain sphere provides detailed information of a beam, itmay be difficult to use in practice for various applications—especiallyon user equipments and network elements not capable of generating thegain sphere data. Codebook characterization measures that work directlyon the sphere, such as spherical cross-correlation, overlap ratio, andenvelope, are approximations that do not capture non-0-lagrelationships. Non-0-lag relationships are derived using non-0-laganalysis which provided an accurate representation of inter-beamrelationships by considering different rotations associated with thearray gain spheres by aligning the spheres on different coordinates, andperforming a separate analysis for each rotation. Moreover, sphereprojections to one-dimension (1D) or two-dimension (2D) representationsmay cause too many features for efficient processing, distortions,and/or discontinuities.

Some aspects of the present disclosure are directed to generatinglow-dimension beam characterization (e.g., capturing detailedinformation provided by a codebook characterization), which may beembodied in vector or other tensor data forms. The low-dimension beamcharacterization may also be referred to herein as an embedding. Theembedding may be generated based on array gain measurements per UE beamon a sphere (e.g., an array gain sphere). In some cases, codebook beamcharacterization metrics per UE beam may be used as an auxiliary inputfor the generation of the embedding. The embedding may be used fordetermining relationships between beams, as an example. The embeddingmay be generated using a ML model trained using various types ofprocessing units, including a graphical processing unit (GPU), centralprocessing unit (CPU), neural processing unit (NPU), artificialintelligence (AI) accelerator, application specific integrated circuit(ASIC), or other processing units, any of which may be “off-target”(e.g., at a network entity such as a network server).

The embedding may then be generated using the trained ML modeloff-target (e.g., using a CPU at the network). For example, a look-uptable may be generated indicating the embedding for each of multiplebeam IDs. The look-up table may be sent to the target (e.g., a UE) to beused for performing various target tasks, such as evaluating inter-beamsimilarities, performing mobility estimation, and performing codebookcharacterization. The embedding may capture beam properties provided bythe codebook characterization, such as width, strength, orientation, andpeak gain, as a few examples. Using the embedding results in reducedstorage space and computation complexity at the UE by providing alow-dimensional representation of a beam that can be stored at the UEand used for performing computations. In some aspects, the training ofthe ML model and generation of the look-up table may be performedon-target (e.g., at a UE).

FIG. 5 illustrates example operations for embedding generation andprocessing. In some aspects, an autoencoder technique may be used toimplement an encoder for generation of embeddings.

An autoencoder is an artificial neural network used to learn efficientdata encodings in an unsupervised manner. The aim of an autoencoder isto learn a representation (encoding) for a set of data by training thenetwork to ignore signal “noise”, in effect generating a lowerdimensionality representation of an input. In some aspects, anautoencoder 590 may be trained end-to-end. The autoencoder 590 mayinclude an encoder 502 that receives a representation of a beam (e.g.,array gain sphere data) and generates a low-dimensional representationof the beam (e.g., an embedding 503). The autoencoder 590 may furtherinclude a decoder 504 that attempts to reconstruct the representation ofthe beam.

In other words, the encoder 502 (also referred to as an embeddinggenerator) may be used to learn efficient encodings to generate theembedding 503 based on one or more training codebooks. In some aspects,target task optimization (or at least improvement) of the embedding maybe performed. For instance, the embedding is provided to the decoder 504to reconstruct (e.g., generate) an output 505 for training by optimizing(or at least improving) an objective function (e.g., by comparing theoutput from the decoder with the input to the encoder and adjustingweights of the autoencoder accordingly).

Once the autoencoder has been trained, the encoder 502 may be usedseparately for other tasks, such as generating encodings, without usingthe decoding aspect of the autoencoder model. For example, the encoder502 may receive a target codebook to generate data for a look-up table510, including an embedding for each beam ID, as shown. The autoencodertraining operations and look-up table generation may be performedoff-target (e.g., on a server at a network). The look-up table may bethen saved and provided to a target 520 (e.g., a UE) for performing atarget task.

For instance, the UE may determine embeddings 514 and 515 associatedwith two beams, respectively (e.g., labeled as UE_beam_id_Rx0 andUE_beam_id_Rx1 in FIG. 5 ) using the look-up table 510. The embeddings514 and 515 may be provided to an ML model 516 to perform a target task,such as a prediction relevant to a communication connection between thetarget (e.g., UE) and the network. The target task may be performedbased on data input 530 as described in more detail herein.

Certain aspects of the present disclosure are directed to techniques forconverting an array gain sphere (or array gain sphere data) to anembedding using a graphical convolution network (GCN). A GCN is a typeof graph neural network designed to perform inference on data structuredas graphs. For example, a graph neural network may be applied directlyto a graph to perform node-level, edge-level, and graph-level predictiontasks. A graph may generally have multiple nodes (e.g., vertices) andedges (e.g., connections between the vertices) that represent therelationship of one node with another. For example, an edge may beassociated with a weight that may indicate importance or cost associatedwith the edge.

To implement a GCN, an array gain sphere may be first converted into agraph data format. Various suitable techniques may be used map the arraygain sphere to a graph. For example, each node of the graph may bemapped to a coordinate (e.g., a phi and theta angle coordinates) on thearray gain sphere. Other techniques may include using an icosahedronsampling of the sphere or a Hierarchical Equal Area isoLatitudePixelization (HEALPix) technique, as described in more detail withrespect to FIGS. 6, 7, and 8 .

FIGS. 6, 7, and 8 illustrate example sampling techniques for mappingdata to a graph data structure.

For example, FIG. 6 illustrates an icosahedron sampling technique. Asshown, an array gain sphere may be divided into sets of equilateraltriangles. Each node in the graph may be mapped to a vertex of one ofthe triangles (e.g., vertex of triangle 602).

FIG. 7 illustrates a HEALPix technique. As shown, the array gain spheremay be divided into hierarchies of equal area curvilinearquadrilaterals. Each quadrilateral may be further divided into multiplequadrilaterals in a hierarchical manner. For example, quadrilateral 706may be divided into quadrilaterals 708, 710, 712, 714, and each of thequadrilaterals 708, 710, 712, 714 may be further divided in smallerquadrilaterals and so on, as shown.

The original array gain sphere may be interpolated from codebook phi andtheta angle points to discretized points. As shown in FIG. 8 , thepoints on the sphere may be then transformed into a set of nodes (e.g.,vertex (V) such as vertex 802) and edges (E) (e.g., edge 804), whichdefine a graph G(V,E). For example, when using the HEALPix technique,the sphere may include a total of 196,608 points for an order-7discretization in accordance with the equation:|V|=12×4^(order),

where |V| is the total number of points. The order of the differentialequation used to define |V| may be 7 for an order-7 discretization. Inthe example of FIG. 8 , graph edges are defined between a node and eachnode of the set of adjacent faces (e.g., quadrilaterals). Nodes aredefined as the centroid of each face (e.g., quadrilateral). Edge 804 maybe defined between vertex 802 and vertex 806. Once the array gain sphereis mapped to a graph, the graph may be fed into one or more graphicalconvolution networks (GCNs) for generation of the embedding, asdescribed in more detail with respect to FIG. 9 .

FIG. 9 illustrates an example technique for implementing an autoencoderusing a graphical convolution network (GCN), in accordance with certainaspects of the present disclosure. As described, an array gain sphere(or array gain sphere data) may be converted into a graph and providedto a GCN to generate an embedding. For example, the graph may beprocessed by GCN 902, GCN 904, GCN 906 to coarsen the graph and generatethe embedding 903, as shown. In other words, GCN 902 may convert anorder 3 graph to an order 2 graph (e.g., representing an order-3discretization), GCN 904 may convert the order 2 graph (e.g.,representing an order-2 discretization) to an order 1 graph (e.g.,representing an order-1 discretization), and GCN 906 may convert theorder 1 graph to order 0 graph. The order 0 graph may be used togenerate the embedding 503. The embedding 903 is provided to a decoderimplemented using GCN 908, GCN 910, GCN 912 to reconstruct the input(the input to GCN 902). The GCNs 908, 910, 912 construct more detailedgraphs. For example, the GCN 908 converts an order 0 graph to an order 1graph, GCN 910 converts an order 1 graph to an order 2 graph, and GCN912 converts an order 2 graph to an order 3 graph, as so on.

FIG. 10 illustrates the generation of an embedding 1003 from adiscretized sphere, in accordance with certain aspects of the presentdisclosure. As described, an array gain sphere may be discretized usinga technique such as the HEALPix technique to generate the discretizedsphere 1004 and coarsened to generate the coarsened discretized sphere1002. The discretized sphere 1002 may be flattened into a 1D vector togenerate the embedding 1003, as shown. For example, the vertex of eachof quadrilaterals on the coarsened discretized sphere 1002 may be usedto generate the embedding 1003.

FIG. 11 illustrates a denoising autoencoder 1100 (e.g., corresponding toautoencoder 590 of FIG. 5 ), in accordance with certain aspects of thepresent disclosure. The addition of noise to a data input has aregularization effect and, in turn, improves the robustness of an MLmodel. As shown, at block 1102, noise may be added to the array gainsphere 1101 to generate a partially corrupted input 1104 (also referredto herein as a noise augmented input). The partially corrupted input1104 may be provided to the encoder 502 to generate the embedding, asdescribed herein. The embedding 503 may be provided to the decoder 504to recover the original uncorrupted input (e.g., array gain sphere1101).

FIG. 12 illustrates techniques for generating an embedding using aFourier transform in accordance with certain aspects of the presentdisclosure. For example, a 2D Fourier transform of the beam gain fromthe phi, theta domain to Fourier domain may be performed.

In the depicted example, the phi and theta angle points from the arraygain sphere 1202 are input to a Fourier transform block 1204 to generatethe embedding 503. For instance, the Fourier coefficients as a 1D vectormay be used as the embedding 1203. The Fourier domain captures dominantenergy in a small set of points, and therefore, generates a compressedrepresentation of the array gain sphere 1202. The rotation in phi andtheta domain may be represented as phase rotation in the Fourier domain.The Fourier transform retains rotation information which may be usefulin downstream tasks such as rotation estimation. Thus, this is analternative to using an autoencoder, as described above, to generate thelow-dimension representation of the array gain sphere 1202 data.

Techniques for Array Gain Sphere Data Processing

Certain aspects of the present disclosure are directed to techniques forprocessing array gain sphere data using an embedding look-up tablegenerated using a trained encoder, for example, as described withrespect to FIG. 5 . The array gain sphere may be processed to performany suitable target task, such as beam rotation, beam comparison, andreceived signal received power (RSRP) prediction, to name just a few.

FIG. 13 illustrates example operations for performing beam rotation inaccordance with certain aspects of the present disclosure. As describedwith respect to FIG. 5 , the encoder 502 may be used to generate valuesfor an embedding look-up table 510 indicating embeddings for one or morebeams. The look-up table is then provided to a UE. In the depictedexample, a UE may use look-up table 1310 to determine an embedding 1314for a specific beam (e.g., represented by array gain sphere 1350). Asshown, the embedding 1314 may be provided to a rotation transformerdecoder 1302 (e.g., an example of ML model 516 of FIG. 5 ). The rotationtransformer decoder 1302 may receive an angle rotation instruction 1306,indicating the angles (e.g., Euler angles) by which the input array gainsphere is to be rotated. The rotation transformer decoder 1302 thengenerates array gain sphere 1304 as rotated in accordance with the anglerotation instruction 1306. The UE may use the rotated array gain sphereto facilitate communications with other nodes.

FIG. 14 illustrates example operations for beam comparison in accordancewith certain aspects of the present disclosure. As shown, embedding 1414and embedding 1415 associated with two beams (e.g., characterized byarray gain spheres 1450 and 1452) may be generated and provided to apairwise difference decoder 1402 (e.g., another example of ML model 516of FIG. 5 ) to generate a pointwise difference parameter indicating thedifference between the two beams (e.g., the difference between the arraygain spheres 1454, 1456). The UE may use the pointwise differenceparameter to facilitate communications with other nodes.

FIG. 15 illustrates example operations for RSRP prediction in accordancewith certain aspects of the present disclosure. In one example, UE RSRPmay be predicated per synchronization signal block (SSB) based on a beamembedding and UE orientation. For example, the embedding 1514 may beprovided to an RSRP predictor 1502 (e.g., another example of ML model516 of FIG. 5 ). The RSRP predictor 1502 may receive an indication of anorientation 1506 of the UE. The RSRP predictor 1502 may then determine(e.g., predict) an RSRP (e.g., each of RSRPs 1508) for each of multiplesynchronization signal blocks (SSBs) (e.g., SSB1 to SSBn, n being aninteger greater than 1) for the given orientation 1506.

In some aspects, a characterization of a beam (e.g., representing anarray gain sphere such as array gain spheres 1350, 1450, 1452) may beprovided to an encoder (e.g., encoder 602) to facilitate training of theencoder for generation of embeddings. For example, a UE may provide thecharacterization of the beam to an encoder locally at the UE (or at aBS), which may be used for training or tuning the encoder (e.g.,facilitating full end-to-end learning/fine-tuning of the encoder toimprove embedding generation).

Example Signal Processing Flow for Data Reuse

FIG. 16 is a flow diagram illustrating example operations 1600 forwireless communication, in accordance with certain aspects of thepresent disclosure. The operations 1600 may be performed, for example,by a wireless node such as a UE (e.g., a UE 104 in the wirelesscommunication network 100 of FIG. 1 ).

Operations 1600 may be implemented as software components that areexecuted and run on one or more processors (e.g., controller/processor280 of FIG. 2 ). Further, the transmission and reception of signals bythe UE in operations 1600 may be enabled, for example, by one or moreantennas (e.g., antennas 252 of FIG. 2 ). In certain aspects, thetransmission and/or reception of signals by the UE may be implementedvia a bus interface of one or more processors (e.g.,controller/processor 280 or beam processing component 281 of FIG. 2 )obtaining and/or outputting signals.

The operations 1600 begin, at block 1602, with the wireless nodereceiving (e.g., from the network) an embedding (e.g., embedding 514 ofFIG. 5 ) representing a characterization associated with a beam. In someaspects, receiving the embedding may include receiving a look-up table(e.g., look-up table 510 of FIG. 5 ) including an indication of theembedding). The characterization may include spherical array gain data,array gain measurements, or codebook beam characterization metrics, toname a few examples.

At block 1604, the wireless node may provide the embedding to a machinelearning (ML) model (e.g., ML model 516 of FIG. 5 ).

At block 1606, the wireless node may generate one or more communicationparameters for communication using the beam via the ML model based onthe embedding. For instance, the ML model may be a rotation transformerdecoder (e.g., rotation transformer decoder 1302 of FIG. 13 ). In thiscase, generating the one or more communication parameters may includedetermining, via the rotation transformer decoder, a rotation of thebeam for the communication based on the embedding. For example, the oneor more communication parameters may include a rotated beam inaccordance with the determination. In some aspects, the wireless nodemay provide a rotation instruction (e.g., rotation instruction 1306 ofFIG. 13 ) to the rotation transformer decoder, and the rotation of thebeam may be determined based on the rotation instruction.

In some aspects, the wireless node may generate another embedding (e.g.,embedding 1415 of FIG. 14 ) based on a characterization of another beam(e.g., array gain sphere 1452 of FIG. 14 ). The embedding (e.g.,embedding 1414 of FIG. 14 ) and the other embedding (e.g., embedding1415 of FIG. 14 ) are provided to a pointwise difference decoder (e.g.,pointwise difference decoder 1402 of FIG. 14 ). The wireless node thengenerates the one or more communication parameters by predicting apointwise difference between the beam and the other beam based on theembedding and the other embedding via the pointwise difference decoder.

In some aspects, the embedding (e.g., embedding 1514 of FIG. 15 ) isprovided to a reference signal receive power (RSRP) decoder (e.g., RSRPpredictor 1502 of FIG. 15 ). The wireless node may generate the one ormore communication parameters by determining, via the RSRP decoder, anRSRP (e.g., each of RSRPs 1508 of FIG. 15 ) for each of multiplesynchronization signal blocks (SSBs) based on the embedding. In someaspects, the wireless node may provide an indication of an orientation(e.g., orientation 1506 of FIG. 15 ) of the wireless node, the RSRPbeing determined based on the orientation.

At block 1608, the wireless node may communicate using the one or morecommunication parameters.

In some aspects, the beam characterization may be input to an encoder(e.g., encoder 602) to facilitate training of the encoder for generationof embeddings. For example, a UE may provide the characterization of thebeam to an encoder locally at the UE (or at a BS), which may be used fortraining or tuning of the encoder to improve embedding generation.

FIG. 17 is a flow diagram illustrating example operations 1700 forwireless communication, in accordance with certain aspects of thepresent disclosure. The operations 1700 may be performed, for example,by a network entity, such as a BS (e.g., such as the BS 102 in thewireless communication network 100 of FIG. 1 ).

Operations 1700 may be implemented as software components that areexecuted and run on one or more processors (e.g., controller/processor240 of FIG. 2 ). Further, the transmission and reception of signals bythe BS in operations 1700 may be enabled, for example, by one or moreantennas (e.g., antennas 234 of FIG. 2 ). In certain aspects, thetransmission and/or reception of signals by the BS may be implementedvia a bus interface of one or more processors (e.g.,controller/processor 240 or training component 241 of FIG. 2 ) obtainingand/or outputting signals.

The operations 1700 begin, at block 1702, by the network entityreceiving a characterization associated with a beam. Thecharacterization may include a spherical array gain data, array gainmeasurements, or codebook beam characterization metrics, to name a fewexamples.

At block 1704, the network entity generates an embedding (e.g.,embedding 503) based on the characterization. In some aspects, thecharacterization comprises a spherical array gain, and the networkentity may convert the spherical array gain to a graph. The embeddingmay be generated using a graph convolution network based on the graph.

In some aspects, the network entity may receiving one or more trainingcodebooks for training of an autoencoder. The embedding may be generatedusing an encoder of the autoencoder. In some aspects, thecharacterization is received as a noise augmented input (e.g., partiallycorrupted input 1104 of FIG. 11 ), and the embedding may be generatedusing a de-noising auto-encoder based on the noise augmented input(e.g., partially corrupted input 1104 of FIG. 11 ). In some aspects,generating the embedding may include down-sampling the characterizationusing a Fourier transform (e.g., at Fourier transform block 1204 of FIG.12 ).

At block 1706, the network entity provides the embedding to a wirelessnode (e.g., a UE). For example, the network entity may provide a look-uptable (e.g., look-up table 510) indicating the embedding.

Example Training Technique

In some aspects of the present disclosure, federated learning may beused to train an autoencoder, such as 590 of FIG. 5 , implemented at thenetwork. Federated learning is a framework that allows training of amodel across multiple nodes that have local data samples without sharingsuch data across nodes but only the parameters of their local models.Federated learning involves using an iteration loop where anorchestrator (e.g., a BS) or central server chooses a model to betrained (e.g., model associated with autoencoder 590 of FIG. 5 ) andtransmits the model to the nodes (e.g. UEs). The nodes train their localcopy of the model with the local data. The orchestrator then pools thelocal models and generates one aggregated model to be transmitted to thenodes. The main benefit of this approach is privacy, as no UE-local datais shared across nodes or with the orchestrator.

In some aspects, a UE may send an embedding of its codebook to the BSfor various use cases. For example, the embedding may be used for amodel to be trained at the BS, or to be used with a pre-trained model togenerate an inference that runs at the BS (e.g., either using uplink(UL) measurements or reported downlink (DL) measurements). Informationon the codebook embedding may include information for mapping such inputto the specific ML model. This provides an opportunity for the actualcodebook design to remain proprietary while only a compressed version ofit can be shared. These use cases can be combined with federatedlearning. A first set of UEs may receive an initial model andparameters. Such UE sets may train a model locally based on theircodebook information and each UE may report back to the BS updatedparameters with or without the UE codebook embedding associated with it.The BS may combine the parameters of these models with or without therespective UE codebook embeddings. The result will be a new model thatthe BS can send to a second set of UEs. This second set of UEs canpossibly include the first set of UEs, in some scenarios. For instance,a set of UEs with higher capability can train locally a model, and theassociated parameters can be reused by different UEs with lowercapability. In the context of models that use Frequency Range 2 (FR2)(e.g., 26-41 GHz) beam measurements as their input, the codebookembedding provides an invariance to be able to leverage learning done bya set of UEs across other UEs. In other words, a codebook embedding maybe derived such that the resulting encoder is codebook/UE independent.Once trained, an encoder trained based on FR2 beam measurements from oneor more UEs can be used across any other UE operating on FR2. For agiven ML model, UEs can communicate their capability, or lack thereof,to train the model locally and participate in the federated learningframework.

Example Wireless Communication Devices

FIG. 18 depicts an example communications device 1800 that includesvarious components operable, configured, or adapted to performoperations for the techniques disclosed herein, such as the operationsdepicted and described with respect to FIG. 17 . In some examples,communication device 1800 may be a base station 102 as described, forexample with respect to FIGS. 1 and 2 .

Communications device 1800 includes a processing system 1802 coupled toa transceiver 1808 (e.g., a transmitter and/or a receiver). Transceiver1808 is configured to transmit (or send) and receive signals for thecommunications device 1800 via an antenna 1810, such as the varioussignals as described herein. Processing system 1802 may be configured toperform processing functions for communications device 1800, includingprocessing signals received and/or to be transmitted by communicationsdevice 1800.

Processing system 1802 includes one or more processors 1820 coupled to acomputer-readable medium/memory 1830 via a bus 1806. In certain aspects,computer-readable medium/memory 1830 is configured to store instructions(e.g., computer-executable code) that when executed by the one or moreprocessors 1820, cause the one or more processors 1820 to perform theoperations illustrated in FIG. 17 , or other operations for performingthe various techniques discussed herein for processing acharacterization of a beam to generate an embedding.

In the depicted example, computer-readable medium/memory 1830 storescode 1831 for receiving, code 1832 for providing, code 1833 forgenerating, code 1834 for transmitting, code 1835 for encoding, and code1837 for training. The code 1835 for encoding may be used to generate alook-up table 1836 stored in the computer-readable medium/memory 1830.

In the depicted example, the one or more processors 1820 includecircuitry configured to implement the code stored in thecomputer-readable medium/memory 1830, including circuitry 1821 forreceiving, circuitry 1822 for providing, circuitry 1823 for generating,circuitry 1824 for transmitting; circuitry 1825 for encoding, andcircuitry 1826 for training. The circuitry 1825 for encoding may be usedto generate a look-up table 1836 stored in the computer-readablemedium/memory 1830.

Various components of communications device 1800 may provide means forperforming the methods described herein, including with respect to FIG.17 .

In some examples, means for transmitting or sending (or means foroutputting for transmission) may include the transceivers 232 and/orantenna(s) 234 of the base station 102 illustrated in FIG. 2 and/ortransceiver 1808 and antenna 1810 of the communication device 1800 inFIG. 18 .

In some examples, means for receiving (or means for obtaining) mayinclude the transceivers 232 and/or antenna(s) 234 of the base stationillustrated in FIG. 2 and/or transceiver 1808 and antenna 1810 of thecommunication device 1800 in FIG. 18 .

In some examples, means for receiving, means for providing, means forgenerating, and means for transmitting may include various processingsystem components, such as: the one or more processors 1820 in FIG. 18 ,or aspects of the base station 102 depicted in FIG. 2 , includingreceive processor 238, transmit processor 220, TX MIMO processor 230,and/or controller/processor 240 (including training component 241).

Notably, FIG. 18 is just use example, and many other examples andconfigurations of communication device 1800 are possible.

FIG. 19 depicts an example communications device 1900 that includesvarious components operable, configured, or adapted to performoperations for the techniques disclosed herein, such as the operationsdepicted and described with respect to FIG. 16 . In some examples,communication device 1900 may be a user equipment 104 as described, forexample with respect to FIGS. 1 and 2 .

Communications device 1900 includes a processing system 1902 coupled toa transceiver 1908 (e.g., a transmitter and/or a receiver). Transceiver1908 is configured to transmit (or send) and receive signals for thecommunications device 1900 via an antenna 1910, such as the varioussignals as described herein. Processing system 1902 may be configured toperform processing functions for communications device 1900, includingprocessing signals received and/or to be transmitted by communicationsdevice 1900.

Processing system 1902 includes one or more processors 1920 coupled to acomputer-readable medium/memory 1930 via a bus 1906. In certain aspects,computer-readable medium/memory 1930 is configured to store instructions(e.g., computer-executable code) that when executed by the one or moreprocessors 1920, cause the one or more processors 1920 to perform theoperations illustrated in FIG. 16 , or other operations for performingthe various techniques discussed herein for processing an embeddingusing machine learning for communication.

In the depicted example, computer-readable medium/memory 1930 storescode 1931 for receiving, code 1932 for generating, code 1933 forproviding, code 1934 for converting; code for RSRP prediction 1935; codefor beam rotation 1936; code 1937 for pairwise difference determination,and code 1939 for training. The code for RSRP prediction 1935; code forbeam rotation 1936; and code 1937 for pairwise difference determinationmay be based on a look-up table 1938 stored in computer-readablemedium/memory 1930.

In the depicted example, the one or more processors 1920 includecircuitry configured to implement the code stored in thecomputer-readable medium/memory 1930, including circuitry 1921 forreceiving, circuitry 1922 for generating, circuitry 1923 for providing,circuitry 1924 for converting, circuitry for RSRP prediction 1925,circuitry for beam rotation 1926, circuitry 1927 for pairwise differencedetermination, and circuitry 1929 for training. The circuitry for RSRPprediction 1925, circuitry for beam rotation 1926, and circuitry 1927for pairwise difference determination may be based on the look-up table1938 stored in computer-readable medium/memory 1930.

Various components of communications device 1900 may provide means forperforming the methods described herein, including with respect to FIG.16 .

In some examples, means for transmitting or sending (or means foroutputting for transmission) may include the transceivers 254 and/orantenna(s) 252 of the user equipment 104 illustrated in FIG. 2 and/ortransceiver 1908 and antenna 1910 of the communication device 1900 inFIG. 19 .

In some examples, means for receiving (or means for obtaining) mayinclude the transceivers 254 and/or antenna(s) 252 of the user equipment104 illustrated in FIG. 2 and/or transceiver 1908 and antenna 1910 ofthe communication device 1900 in FIG. 19 .

In some examples, means for receiving, means for providing, means forgenerating, and means for providing may include various processingsystem components, such as: the one or more processors 1920 in FIG. 19 ,or aspects of the user equipment 104 depicted in FIG. 2 , includingreceive processor 258, transmit processor 264, TX MIMO processor 266,and/or controller/processor 280 (including beam processing component281).

Notably, FIG. 19 is just use example, and many other examples andconfigurations of communication device 1900 are possible.

Example Clauses

Implementation examples are described in the following numbered clauses:

Clause 1. A method for wireless communication by a wireless node,comprising: receiving an embedding representing a characterizationassociated with a beam; providing the embedding to a machine learning(ML) model; generating one or more communication parameters forcommunication using the beam via the ML model based on the embedding;and communicating using the one or more communication parameters.

Clause 2. The method of clause 1, wherein receiving the embeddingcomprises receiving a look-up table indicating the embedding.

Clause 3. The method of one of clauses 1-2, wherein: the ML modelcomprises a rotation transformer decoder; generating the one or morecommunication parameters comprises determining, via the rotationtransformer decoder, a rotation of the beam for the communication basedon the embedding; and the one or more communication parameters comprisesa rotated beam in accordance with the determination.

Clause 4. The method of clause 3, further comprising providing arotation instruction to the rotation transformer decoder, wherein therotation of the beam is determined based on the rotation instruction.

Clause 5. The method of one of clauses 1-4, wherein: the method furthercomprises generating another embedding based on a characterization ofanother beam; the embedding and the other embedding are provided to apointwise difference decoder; and generating the one or morecommunication parameters comprises predicting a pointwise differencebetween the beam and the other beam based on the embedding and the otherembedding via the pointwise difference decoder.

Clause 6. The method of one of clauses 1-5, wherein: the embedding isprovided to a reference signal receive power (RSRP) decoder; andgenerating the one or more communication parameters comprisesdetermining, via the RSRP decoder, an RSRP for each of multiplesynchronization signal blocks (SSBs) based on the embedding.

Clause 7. The method of clause 6, further comprising providing anindication of an orientation of the wireless node, wherein the RSRP isdetermined based on the orientation.

Clause 8. The method of one of clauses 1-7, wherein the characterizationcomprises a spherical array gain.

Clause 9. The method of one of clauses 1-8, wherein the characterizationcomprises array gain measurements or codebook beam characterizationmetrics.

Clause 10. The method of one of clauses 1-9, further comprising trainingan encoder configured to generate embeddings using the characterizationassociated with the beam.

Clause 11. A method for wireless communication, comprising: receiving acharacterization associated with a beam; generating an embedding basedon the characterization; and providing the embedding to a wireless node.

Clause 12. The method of clause 11, wherein providing the embeddingcomprises providing a look-up table indicating the embedding associatedwith the beam.

Clause 13. The method of one of clauses 11-12, wherein: thecharacterization comprises a spherical array gain; the method furthercomprises converting the spherical array gain to a graph; and theembedding is generated using a graph convolution network based on thegraph.

Clause 14. The method of one of clauses 11-13, further comprisingreceiving one or more training codebooks for training of an autoencoder,wherein the embedding is generated using an encoder of the autoencoder.

Clause 15. The method of one of clauses 11-14, wherein thecharacterization comprises array gain measurements or codebook beamcharacterization metrics.

Clause 16. The method of clause 15, wherein the characterization isreceived as a noise augmented input, and wherein the embedding isgenerated using a de-noising auto-encoder based on the noise augmentedinput.

Clause 17. The method of one of clauses 11-16, wherein generating theembedding comprises down sampling the characterization using a Fouriertransform.

Clause 18. The method of one of clauses 11-17, wherein thecharacterization comprises a spherical array gain.

Clause 19. The method of one of clauses 11-18, further comprisingtraining an autoencoder using a federated learning model, wherein theembedding is generated using an encoder of the autoencoder.

Clause 20: An apparatus, comprising: a memory comprising executableinstructions; one or more processors configured to execute theexecutable instructions and cause the apparatus to perform a method inaccordance with any one of Clauses 1-19.

Clause 21: An apparatus, comprising means for performing a method inaccordance with any one of Clauses 1-19.

Clause 23: A non-transitory computer-readable medium comprisingexecutable instructions that, when executed by one or more processors ofan apparatus, cause the apparatus to perform a method in accordance withany one of Clauses 1-19.

Clause 24: A computer program product embodied on a computer-readablestorage medium comprising code for performing a method in accordancewith any one of Clauses 1-19.

Additional Wireless Communication Network Considerations

The techniques and methods described herein may be used for variouswireless communications networks (or wireless wide area network (WWAN))and radio access technologies (RATs). While aspects may be describedherein using terminology commonly associated with 3G, 4G, and/or 5G(e.g., 5G new radio (NR)) wireless technologies, aspects of the presentdisclosure may likewise be applicable to other communication systems andstandards not explicitly mentioned herein.

5G wireless communication networks may support various advanced wirelesscommunication services, such as enhanced mobile broadband (eMBB),millimeter wave (mmWave), machine type communications (MTC), and/ormission critical targeting ultra-reliable, low-latency communications(URLLC). These services, and others, may include latency and reliabilityrequirements.

Returning to FIG. 1 , various aspects of the present disclosure may beperformed within the example wireless communication network 100.

In 3GPP, the term “cell” can refer to a coverage area of a NodeB and/ora narrowband subsystem serving this coverage area, depending on thecontext in which the term is used. In NR systems, the term “cell” andBS, next generation NodeB (gNB or gNodeB), access point (AP),distributed unit (DU), carrier, or transmission reception point may beused interchangeably. A BS may provide communication coverage for amacro cell, a pico cell, a femto cell, and/or other types of cells.

A macro cell may generally cover a relatively large geographic area(e.g., several kilometers in radius) and may allow unrestricted accessby UEs with service subscription. A pico cell may cover a relativelysmall geographic area (e.g., a sports stadium) and may allowunrestricted access by UEs with service subscription. A femto cell maycover a relatively small geographic area (e.g., a home) and may allowrestricted access by UEs having an association with the femto cell(e.g., UEs in a Closed Subscriber Group (CSG) and UEs for users in thehome). A BS for a macro cell may be referred to as a macro BS. A BS fora pico cell may be referred to as a pico BS. A BS for a femto cell maybe referred to as a femto BS, home BS, or a home NodeB.

Base stations 102 configured for 4G LTE (collectively referred to asEvolved Universal Mobile Telecommunications System (UMTS) TerrestrialRadio Access Network (E-UTRAN)) may interface with the EPC 160 throughfirst backhaul links 132 (e.g., an S1 interface). Base stations 102configured for 5G (e.g., 5G NR or Next Generation RAN (NG-RAN)) mayinterface with 5GC 190 through second backhaul links 184. Base stations102 may communicate directly or indirectly (e.g., through the EPC 160 or5GC 190) with each other over third backhaul links 134 (e.g., X2interface). Third backhaul links 134 may generally be wired or wireless.

Small cell 102′ may operate in a licensed and/or an unlicensed frequencyspectrum. When operating in an unlicensed frequency spectrum, the smallcell 102′ may employ NR and use the same 5 GHz unlicensed frequencyspectrum as used by the Wi-Fi AP 150. Small cell 102′, employing NR inan unlicensed frequency spectrum, may boost coverage to and/or increasecapacity of the access network.

Some base stations, such as gNB 180 may operate in a traditional sub-6GHz spectrum, in millimeter wave (mmWave) frequencies, and/or nearmmWave frequencies in communication with the UE 104. When the gNB 180operates in mmWave or near mmWave frequencies, the gNB 180 may bereferred to as an mmWave base station.

The communication links 120 between base stations 102 and, for example,UEs 104, may be through one or more carriers. For example, base stations102 and UEs 104 may use spectrum up to YMHz (e.g., 5, 10, 15, 20, 100,400, and other MHz) bandwidth per carrier allocated in a carrieraggregation of up to a total of Yx MHz (x component carriers) used fortransmission in each direction. The carriers may or may not be adjacentto each other. Allocation of carriers may be asymmetric with respect toDL and UL (e.g., more or fewer carriers may be allocated for DL than forUL). The component carriers may include a primary component carrier andone or more secondary component carriers. A primary component carriermay be referred to as a primary cell (PCell) and a secondary componentcarrier may be referred to as a secondary cell (SCell).

Wireless communications system 100 further includes a Wi-Fi access point(AP) 150 in communication with Wi-Fi stations (STAs) 152 viacommunication links 154 in, for example, a 2.4 GHz and/or 5 GHzunlicensed frequency spectrum. When communicating in an unlicensedfrequency spectrum, the STAs 152/AP 150 may perform a clear channelassessment (CCA) prior to communicating in order to determine whetherthe channel is available.

Certain UEs 104 may communicate with each other using device-to-device(D2D) communication link 158. The D2D communication link 158 may use theDL/UL WWAN spectrum. The D2D communication link 158 may use one or moresidelink channels, such as a physical sidelink broadcast channel(PSBCH), a physical sidelink discovery channel (PSDCH), a physicalsidelink shared channel (PSSCH), and a physical sidelink control channel(PSCCH). D2D communication may be through a variety of wireless D2Dcommunications systems, such as for example, FlashLinQ, WiMedia,Bluetooth, ZigBee, Wi-Fi based on the IEEE 802.11 standard, 4G (e.g.,LTE), or 5G (e.g., NR), to name a few options.

EPC 160 may include a Mobility Management Entity (MME) 162, other MMEs164, a Serving Gateway 166, a Multimedia Broadcast Multicast Service(MBMS) Gateway 168, a Broadcast Multicast Service Center (BM-SC) 170,and a Packet Data Network (PDN) Gateway 172. MME 162 may be incommunication with a Home Subscriber Server (HSS) 174. MME 162 is thecontrol node that processes the signaling between the UEs 104 and theEPC 160. Generally, MME 162 provides bearer and connection management.

Generally, user Internet protocol (IP) packets are transferred throughServing Gateway 166, which itself is connected to PDN Gateway 172. PDNGateway 172 provides UE IP address allocation as well as otherfunctions. PDN Gateway 172 and the BM-SC 170 are connected to the IPServices 176, which may include, for example, the Internet, an intranet,an IP Multimedia Subsystem (IMS), a PS Streaming Service, and/or otherIP services.

BM-SC 170 may provide functions for MBMS user service provisioning anddelivery. BM-SC 170 may serve as an entry point for content providerMBMS transmission, may be used to authorize and initiate MBMS BearerServices within a public land mobile network (PLMN), and may be used toschedule MBMS transmissions. MBMS Gateway 168 may be used to distributeMBMS traffic to the base stations 102 belonging to a Multicast BroadcastSingle Frequency Network (MBSFN) area broadcasting a particular service,and may be responsible for session management (start/stop) and forcollecting eMBMS related charging information.

5GC 190 may include an Access and Mobility Management Function (AMF)192, other AMFs 193, a Session Management Function (SMF) 194, and a UserPlane Function (UPF) 195. AMF 192 may be in communication with a UnifiedData Management (UDM) 196.

AMF 192 is generally the control node that processes the signalingbetween UEs 104 and 5GC 190. Generally, AMF 192 provides QoS flow andsession management.

All user Internet protocol (IP) packets are transferred through UPF 195,which is connected to the IP Services 197, and which provides UE IPaddress allocation as well as other functions for 5GC 190. IP Services197 may include, for example, the Internet, an intranet, an IPMultimedia Subsystem (IMS), a PS Streaming Service, and/or other IPservices.

Returning to FIG. 2 , various example components of BS 102 and UE 104(e.g., the wireless communication network 100 of FIG. 1 ) are depicted,which may be used to implement aspects of the present disclosure.

At BS 102, a transmit processor 220 may receive data from a data source212 and control information from a controller/processor 240. The controlinformation may be for the physical broadcast channel (PBCH), physicalcontrol format indicator channel (PCFICH), physical hybrid ARQ indicatorchannel (PHICH), physical downlink control channel (PDCCH), group commonPDCCH (GC PDCCH), and others. The data may be for the physical downlinkshared channel (PDSCH), in some examples.

A medium access control (MAC)-control element (MAC-CE) is a MAC layercommunication structure that may be used for control command exchangebetween wireless nodes. The MAC-CE may be carried in a shared channelsuch as a physical downlink shared channel (PDSCH), a physical uplinkshared channel (PUSCH), or a physical sidelink shared channel (PSSCH).

Processor 220 may process (e.g., encode and symbol map) the data andcontrol information to obtain data symbols and control symbols,respectively. Transmit processor 220 may also generate referencesymbols, such as for the primary synchronization signal (PSS), secondarysynchronization signal (SSS), PBCH demodulation reference signal (DMRS),and channel state information reference signal (CSI-RS).

Transmit (TX) multiple-input multiple-output (MIMO) processor 230 mayperform spatial processing (e.g., precoding) on the data symbols, thecontrol symbols, and/or the reference symbols, if applicable, and mayprovide output symbol streams to the modulators (MODs) in transceivers232 a-232 t. Each modulator in transceivers 232 a-232 t may process arespective output symbol stream (e.g., for OFDM) to obtain an outputsample stream. Each modulator may further process (e.g., convert toanalog, amplify, filter, and upconvert) the output sample stream toobtain a downlink signal. Downlink signals from the modulators intransceivers 232 a-232 t may be transmitted via the antennas 234 a-234t, respectively.

At UE 104, antennas 252 a-252 r may receive the downlink signals fromthe BS 102 and may provide received signals to the demodulators (DEMODs)in transceivers 254 a-254 r, respectively. Each demodulator intransceivers 254 a-254 r may condition (e.g., filter, amplify,downconvert, and digitize) a respective received signal to obtain inputsamples. Each demodulator may further process the input samples (e.g.,for OFDM) to obtain received symbols.

MIMO detector 256 may obtain received symbols from all the demodulatorsin transceivers 254 a-254 r, perform MIMO detection on the receivedsymbols if applicable, and provide detected symbols. Receive processor258 may process (e.g., demodulate, deinterleave, and decode) thedetected symbols, provide decoded data for the UE 104 to a data sink260, and provide decoded control information to a controller/processor280.

On the uplink, at UE 104, transmit processor 264 may receive and processdata (e.g., for the physical uplink shared channel (PUSCH)) from a datasource 262 and control information (e.g., for the physical uplinkcontrol channel (PUCCH) from the controller/processor 280. Transmitprocessor 264 may also generate reference symbols for a reference signal(e.g., for the sounding reference signal (SRS)). The symbols from thetransmit processor 264 may be precoded by a TX MIMO processor 266 ifapplicable, further processed by the modulators in transceivers 254a-254 r (e.g., for SC-FDM), and transmitted to BS 102.

At BS 102, the uplink signals from UE 104 may be received by antennas234 a-t, processed by the demodulators in transceivers 232 a-232 t,detected by a MIMO detector 236 if applicable, and further processed bya receive processor 238 to obtain decoded data and control informationsent by UE 104. Receive processor 238 may provide the decoded data to adata sink 239 and the decoded control information to thecontroller/processor 240.

Memories 242 and 282 may store data and program codes for BS 102 and UE104, respectively.

Scheduler 244 may schedule UEs for data transmission on the downlinkand/or uplink.

5G may utilize orthogonal frequency division multiplexing (OFDM) with acyclic prefix (CP) on the uplink and downlink. 5G may also supporthalf-duplex operation using time division duplexing (TDD). OFDM andsingle-carrier frequency division multiplexing (SC-FDM) partition thesystem bandwidth into multiple orthogonal subcarriers, which are alsocommonly referred to as tones and bins. Each subcarrier may be modulatedwith data. Modulation symbols may be sent in the frequency domain withOFDM and in the time domain with SC-FDM. The spacing between adjacentsubcarriers may be fixed, and the total number of subcarriers may bedependent on the system bandwidth. The minimum resource allocation,called a resource block (RB), may be 12 consecutive subcarriers in someexamples. The system bandwidth may also be partitioned into subbands.For example, a subband may cover multiple RBs. NR may support a basesubcarrier spacing (SCS) of 15 KHz and other SCS may be defined withrespect to the base SCS (e.g., 30 kHz, 60 kHz, 120 kHz, 240 kHz, andothers).

As above, FIGS. 3A-3D depict various example aspects of data structuresfor a wireless communication network, such as wireless communicationnetwork 100 of FIG. 1 .

In various aspects, the 5G frame structure may be frequency divisionduplex (FDD), in which for a particular set of subcarriers (carriersystem bandwidth), subframes within the set of subcarriers are dedicatedfor either DL or UL. 5G frame structures may also be time divisionduplex (TDD), in which for a particular set of subcarriers (carriersystem bandwidth), subframes within the set of subcarriers are dedicatedfor both DL and UL. In the examples provided by FIGS. 3A and 3C, the 5Gframe structure is assumed to be TDD, with subframe 4 being configuredwith slot format 28 (with mostly DL), where D is DL, U is UL, and X isflexible for use between DL/UL, and subframe 3 being configured withslot format 34 (with mostly UL). While subframes 3, 4 are shown withslot formats 34, 28, respectively, any particular subframe may beconfigured with any of the various available slot formats 0-61. Slotformats 0, 1 are all DL, UL, respectively. Other slot formats 2-61include a mix of DL, UL, and flexible symbols. UEs are configured withthe slot format (dynamically through DL control information (DCI), orsemi-statically/statically through radio resource control (RRC)signaling) through a received slot format indicator (SFI). Note that thedescription below applies also to a 5G frame structure that is TDD.

Other wireless communication technologies may have a different framestructure and/or different channels. A frame (10 ms) may be divided into10 equally sized subframes (1 ms). Each subframe may include one or moretime slots. Subframes may also include mini-slots, which may include 7,4, or 2 symbols. In some examples, each slot may include 7 or 14symbols, depending on the slot configuration.

For example, for slot configuration 0, each slot may include 14 symbols,and for slot configuration 1, each slot may include 7 symbols. Thesymbols on DL may be cyclic prefix (CP) OFDM (CP-OFDM) symbols. Thesymbols on UL may be CP-OFDM symbols (for high throughput scenarios) ordiscrete Fourier transform (DFT) spread OFDM (DFT-s-OFDM) symbols (alsoreferred to as single carrier frequency-division multiple access(SC-FDMA) symbols) (for power limited scenarios; limited to a singlestream transmission).

The number of slots within a subframe is based on the slot configurationand the numerology. For slot configuration 0, different numerologies (μ)0 to 5 allow for 1, 2, 4, 8, 16, and 32 slots, respectively, persubframe. For slot configuration 1, different numerologies 0 to 2 allowfor 2, 4, and 8 slots, respectively, per subframe. Accordingly, for slotconfiguration 0 and numerology μ, there are 14 symbols/slot and 2μslots/subframe. The subcarrier spacing and symbol length/duration are afunction of the numerology. The subcarrier spacing may be equal to2^(μ)×15 kHz, where μ is the numerology 0 to 5. As such, the numerologyμ=0 has a subcarrier spacing of 15 kHz and the numerology μ=5 has asubcarrier spacing of 480 kHz. The symbol length/duration is inverselyrelated to the subcarrier spacing. FIGS. 3A-3D provide an example ofslot configuration 0 with 14 symbols per slot and numerology μ=2 with 4slots per subframe. The slot duration is 0.25 ms, the subcarrier spacingis 60 kHz, and the symbol duration is approximately 16.67 μs.

A resource grid may be used to represent the frame structure. Each timeslot includes a resource block (RB) (also referred to as physical RBs(PRBs)) that extends 12 consecutive subcarriers. The resource grid isdivided into multiple resource elements (REs). The number of bitscarried by each RE depends on the modulation scheme.

As illustrated in FIG. 3A, some of the REs carry reference (pilot)signals (RS) for a UE (e.g., UE 104 of FIGS. 1 and 2 ). The RS mayinclude demodulation RS (DM-RS) (indicated as Rx for one particularconfiguration, where 100x is the port number, but other DM-RSconfigurations are possible) and channel state information referencesignals (CSI-RS) for channel estimation at the UE. The RS may alsoinclude beam measurement RS (BRS), beam refinement RS (BRRS), and phasetracking RS (PT-RS).

FIG. 3B illustrates an example of various DL channels within a subframeof a frame. The physical downlink control channel (PDCCH) carries DCIwithin one or more control channel elements (CCEs), each CCE includingnine RE groups (REGs), each REG including four consecutive REs in anOFDM symbol.

A primary synchronization signal (PSS) may be within symbol 2 ofparticular subframes of a frame. The PSS is used by a UE (e.g., 104 ofFIGS. 1 and 2 ) to determine subframe/symbol timing and a physical layeridentity.

A secondary synchronization signal (SSS) may be within symbol 4 ofparticular subframes of a frame. The SSS is used by a UE to determine aphysical layer cell identity group number and radio frame timing.

Based on the physical layer identity and the physical layer cellidentity group number, the UE can determine a physical cell identifier(PCI). Based on the PCI, the UE can determine the locations of theaforementioned DM-RS. The physical broadcast channel (PBCH), whichcarries a master information block (MIB), may be logically grouped withthe PSS and SSS to form a synchronization signal (SS)/PBCH block. TheMIB provides a number of RBs in the system bandwidth and a system framenumber (SFN). The physical downlink shared channel (PDSCH) carries userdata, broadcast system information not transmitted through the PBCH suchas system information blocks (SIBs), and paging messages.

As illustrated in FIG. 3C, some of the REs carry DM-RS (indicated as Rfor one particular configuration, but other DM-RS configurations arepossible) for channel estimation at the base station. The UE maytransmit DM-RS for the physical uplink control channel (PUCCH) and DM-RSfor the physical uplink shared channel (PUSCH). The PUSCH DM-RS may betransmitted in the first one or two symbols of the PUSCH. The PUCCHDM-RS may be transmitted in different configurations depending onwhether short or long PUCCHs are transmitted and depending on theparticular PUCCH format used. The UE may transmit sounding referencesignals (SRS). The SRS may be transmitted in the last symbol of asubframe. The SRS may have a comb structure, and a UE may transmit SRSon one of the combs. The SRS may be used by a base station for channelquality estimation to enable frequency-dependent scheduling on the UL.

FIG. 3D illustrates an example of various UL channels within a subframeof a frame. The PUCCH may be located as indicated in one configuration.The PUCCH carries uplink control information (UCI), such as schedulingrequests, a channel quality indicator (CQI), a precoding matrixindicator (PMI), a rank indicator (RI), and HARQ ACK/NACK feedback. ThePUSCH carries data, and may additionally be used to carry a bufferstatus report (BSR), a power headroom report (PHR), and/or UCI.

Additional Considerations

The preceding description provides examples of techniques for generationand processing of an embedding representing a beam in communicationsystems. The preceding description is provided to enable any personskilled in the art to practice the various aspects described herein. Theexamples discussed herein are not limiting of the scope, applicability,or aspects set forth in the claims. Various modifications to theseaspects will be readily apparent to those skilled in the art, and thegeneric principles defined herein may be applied to other aspects. Forexample, changes may be made in the function and arrangement of elementsdiscussed without departing from the scope of the disclosure. Variousexamples may omit, substitute, or add various procedures or componentsas appropriate. For instance, the methods described may be performed inan order different from that described, and various steps may be added,omitted, or combined. Also, features described with respect to someexamples may be combined in some other examples. For example, anapparatus may be implemented or a method may be practiced using anynumber of the aspects set forth herein. In addition, the scope of thedisclosure is intended to cover such an apparatus or method that ispracticed using other structure, functionality, or structure andfunctionality in addition to, or other than, the various aspects of thedisclosure set forth herein. It should be understood that any aspect ofthe disclosure disclosed herein may be embodied by one or more elementsof a claim.

The techniques described herein may be used for various wirelesscommunication technologies, such as 5G (e.g., 5G NR), 3GPP Long TermEvolution (LTE), LTE-Advanced (LTE-A), code division multiple access(CDMA), time division multiple access (TDMA), frequency divisionmultiple access (FDMA), orthogonal frequency division multiple access(OFDMA), single-carrier frequency division multiple access (SC-FDMA),time division synchronous code division multiple access (TD-SCDMA), andother networks. The terms “network” and “system” are often usedinterchangeably. A CDMA network may implement a radio technology such asUniversal Terrestrial Radio Access (UTRA), cdma2000, and others. UTRAincludes Wideband CDMA (WCDMA) and other variants of CDMA. cdma2000covers IS-2000, IS-95 and IS-856 standards. A TDMA network may implementa radio technology such as Global System for Mobile Communications(GSM). An OFDMA network may implement a radio technology such as NR(e.g. 5G RA), Evolved UTRA (E-UTRA), Ultra Mobile Broadband (UMB), IEEE802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDMA, andothers. UTRA and E-UTRA are part of Universal Mobile TelecommunicationSystem (UMTS). LTE and LTE-A are releases of UMTS that use E-UTRA. UTRA,E-UTRA, UMTS, LTE, LTE-A and GSM are described in documents from anorganization named “3rd Generation Partnership Project” (3GPP). cdma2000and UMB are described in documents from an organization named “3rdGeneration Partnership Project 2” (3GPP2). NR is an emerging wirelesscommunications technology under development.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a general purpose processor, a DSP, an ASIC, a fieldprogrammable gate array (FPGA) or other programmable logic device (PLD),discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general-purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, a system on a chip(SoC), or any other such configuration.

If implemented in hardware, an example hardware configuration maycomprise a processing system in a wireless node. The processing systemmay be implemented with a bus architecture. The bus may include anynumber of interconnecting buses and bridges depending on the specificapplication of the processing system and the overall design constraints.The bus may link together various circuits including a processor,machine-readable media, and a bus interface. The bus interface may beused to connect a network adapter, among other things, to the processingsystem via the bus. The network adapter may be used to implement thesignal processing functions of the PHY layer. In the case of a userequipment (see FIG. 1 ), a user interface (e.g., keypad, display, mouse,joystick, touchscreen, biometric sensor, proximity sensor, lightemitting element, and others) may also be connected to the bus. The busmay also link various other circuits such as timing sources,peripherals, voltage regulators, power management circuits, and thelike, which are well known in the art, and therefore, will not bedescribed any further. The processor may be implemented with one or moregeneral-purpose and/or special-purpose processors. Examples includemicroprocessors, microcontrollers, DSP processors, and other circuitrythat can execute software. Those skilled in the art will recognize howbest to implement the described functionality for the processing systemdepending on the particular application and the overall designconstraints imposed on the overall system.

If implemented in software, the functions may be stored or transmittedover as one or more instructions or code on a computer readable medium.Software shall be construed broadly to mean instructions, data, or anycombination thereof, whether referred to as software, firmware,middleware, microcode, hardware description language, or otherwise.Computer-readable media include both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. The processor may beresponsible for managing the bus and general processing, including theexecution of software modules stored on the machine-readable storagemedia. A computer-readable storage medium may be coupled to a processorsuch that the processor can read information from, and write informationto, the storage medium. In the alternative, the storage medium may beintegral to the processor. By way of example, the machine-readable mediamay include a transmission line, a carrier wave modulated by data,and/or a computer readable storage medium with instructions storedthereon separate from the wireless node, all of which may be accessed bythe processor through the bus interface. Alternatively, or in addition,the machine-readable media, or any portion thereof, may be integratedinto the processor, such as the case may be with cache and/or generalregister files. Examples of machine-readable storage media may include,by way of example, RAM (Random Access Memory), flash memory, ROM (ReadOnly Memory), PROM (Programmable Read-Only Memory), EPROM (ErasableProgrammable Read-Only Memory), EEPROM (Electrically ErasableProgrammable Read-Only Memory), registers, magnetic disks, opticaldisks, hard drives, or any other suitable storage medium, or anycombination thereof. The machine-readable media may be embodied in acomputer-program product.

A software module may comprise a single instruction, or manyinstructions, and may be distributed over several different codesegments, among different programs, and across multiple storage media.The computer-readable media may comprise a number of software modules.The software modules include instructions that, when executed by anapparatus such as a processor, cause the processing system to performvarious functions. The software modules may include a transmissionmodule and a receiving module. Each software module may reside in asingle storage device or be distributed across multiple storage devices.By way of example, a software module may be loaded into RAM from a harddrive when a triggering event occurs. During execution of the softwaremodule, the processor may load some of the instructions into cache toincrease access speed. One or more cache lines may then be loaded into ageneral register file for execution by the processor. When referring tothe functionality of a software module below, it will be understood thatsuch functionality is implemented by the processor when executinginstructions from that software module.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover a, b, c,a-b, a-c, b-c, and a-b-c, as well as any combination with multiples ofthe same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b,b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like.

The methods disclosed herein comprise one or more steps or actions forachieving the methods. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims. Further, thevarious operations of methods described above may be performed by anysuitable means capable of performing the corresponding functions. Themeans may include various hardware and/or software component(s) and/ormodule(s), including, but not limited to a circuit, an applicationspecific integrated circuit (ASIC), or processor. Generally, where thereare operations illustrated in figures, those operations may havecorresponding counterpart means-plus-function components with similarnumbering.

The following claims are not intended to be limited to the aspects shownherein, but are to be accorded the full scope consistent with thelanguage of the claims. Within a claim, reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” Unless specifically statedotherwise, the term “some” refers to one or more. No claim element is tobe construed under the provisions of 35 U.S.C. § 112(f) unless theelement is expressly recited using the phrase “means for” or, in thecase of a method claim, the element is recited using the phrase “stepfor.” All structural and functional equivalents to the elements of thevarious aspects described throughout this disclosure that are known orlater come to be known to those of ordinary skill in the art areexpressly incorporated herein by reference and are intended to beencompassed by the claims. Moreover, nothing disclosed herein isintended to be dedicated to the public regardless of whether suchdisclosure is explicitly recited in the claims.

What is claimed is:
 1. A method for wireless communication by a wirelessnode, comprising: receiving an embedding representing a characterizationassociated with a beam, wherein the characterization comprises arraygain measurements or codebook beam characterization metrics; providingthe embedding to a machine learning (ML) model; generating one or morecommunication parameters for communication using the beam via the MLmodel based on the embedding; communicating using the one or morecommunication parameters; and training an encoder configured to generateembeddings using the characterization associated with the beam.
 2. Themethod of claim 1, wherein receiving the embedding comprises receiving alook-up table indicating the embedding.
 3. The method of claim 1,wherein: the ML model comprises a rotation transformer decoder;generating the one or more communication parameters comprisesdetermining, via the rotation transformer decoder, a rotation of thebeam for the communication based on the embedding; the one or morecommunication parameters comprises a rotated beam in accordance with thedetermination; and the method further comprises providing a rotationinstruction to the rotation transformer decoder wherein the rotation ofthe beam is determined based on the rotation instruction.
 4. The methodof claim 1, wherein: the method further comprises generating anotherembedding based on a characterization of another beam; the embedding andthe other embedding are provided to a pointwise difference decoder; andgenerating the one or more communication parameters comprises predictinga pointwise difference between the beam and the other beam based on theembedding and the other embedding via the pointwise difference decoder.5. The method of claim 1, wherein: the embedding is provided to areference signal receive power (RSRP) decoder; generating the one ormore communication parameters comprises determining, via the RSRPdecoder, an RSRP for each of multiple synchronization signal blocks(SSBs) based on the embedding; and the method further comprisesproviding an indication of an orientation of the wireless node, whereinthe RSRP is determined based on the orientation.
 6. The method of claim1, wherein the characterization comprises a spherical array gain.
 7. Themethod of claim 1, wherein the array gain measurements correspond toarray gain measurements per beam on a sphere, and the codebook beamcharacterization metrics correspond to codebook beam characterizationmetrics per beam as an auxiliary input for the embedding.
 8. A methodfor wireless communication, comprising: receiving a characterizationassociated with a beam, wherein the characterization comprises arraygain measurements or codebook beam characterization metrics; generatingan embedding based on the characterization; providing the embedding to awireless node; and training an autoencoder using a federated learningmodel, wherein the embedding is generated using an encoder of theautoencoder.
 9. The method of claim 8, wherein providing the embeddingcomprises providing a look-up table indicating the embedding associatedwith the beam.
 10. The method of claim 8, wherein: the characterizationcomprises a spherical array gain; the method further comprisesconverting the spherical array gain to a graph; and the embedding isgenerated using a graph convolution network based on the graph.
 11. Themethod of claim 8, further comprising receiving one or more trainingcodebooks for training of the autoencoder.
 12. The method of claim 8,wherein the array gain measurements correspond to array gainmeasurements per beam on a sphere, and the codebook beamcharacterization metrics correspond to codebook beam characterizationmetrics per beam as an auxiliary input for the embedding, wherein thecharacterization is received as a noise augmented input, and wherein theembedding is generated using a de-noising auto-encoder based on thenoise augmented input.
 13. The method of claim 8, wherein generating theembedding comprises down sampling the characterization using a Fouriertransform.
 14. The method of claim 8, wherein the characterizationcomprises a spherical array gain.
 15. An apparatus for wirelesscommunication by a wireless node, comprising: a memory comprisingexecutable instructions; and one or more processors configured toexecute the executable instructions and cause the apparatus to: receivean embedding representing a characterization associated with a beam,wherein the characterization comprises array gain measurements orcodebook beam characterization metrics; provide the embedding to amachine learning (ML) model; generate one or more communicationparameters for communication using the beam via the ML model based onthe embedding; communicate using the one or more communicationparameters, and train an encoder configured to generate embeddings usingthe characterization associated with the beam.
 16. The apparatus ofclaim 15, wherein, in causing the apparatus to receive the embedding,the one or more processors are configured to cause the apparatus toreceive a look-up table indicating the embedding.
 17. The apparatus ofclaim 15, wherein: the ML model comprises a rotation transformerdecoder; in causing the apparatus to generate the one or morecommunication parameters, the one or more processors are configured tocause the apparatus to determine, via the rotation transformer decoder,a rotation of the beam for the communication based on the embedding; theone or more communication parameters comprises a rotated beam inaccordance with the determination; and the one or more processors arefurther configured to cause the apparatus to provide a rotationinstruction to the rotation transformer decoder, wherein the rotation ofthe beam is determined based on the rotation instruction.
 18. Theapparatus of claim 15, wherein: the one or more processors are furtherconfigured to cause the apparatus to: generate another embedding basedon a characterization of another beam; and provide the embedding and theother embedding to a pointwise difference decoder; and in causing theapparatus to generate the one or more communication parameters, the oneor more processors are configured to cause the apparatus to predict apointwise difference between the beam and the other beam based on theembedding and the other embedding via the pointwise difference decoder.19. The apparatus of claim 15, wherein: the one or more processors arefurther configured to cause the apparatus to provide the embedding to areference signal receive power (RSRP) decoder; in causing the apparatusto generate the one or more communication parameters, the one or moreprocessors are configured to cause the apparatus to determine, via theRSRP decoder, an RSRP for each of multiple synchronization signal blocks(SSBs) based on the embedding; and the one or more processors arefurther configured to cause the apparatus to provide an indication of anorientation of the wireless node, wherein the RSRP is determined basedon the orientation.
 20. The apparatus of claim 15, wherein thecharacterization comprises a spherical array gain.
 21. The apparatus ofclaim 15, wherein the array gain measurements correspond to array gainmeasurements per beam on a sphere, and the codebook beamcharacterization metrics correspond to codebook beam characterizationmetrics per beam as an auxiliary input for the embedding.
 22. Anapparatus for wireless communication, comprising: a memory comprisingexecutable instructions; and one or more processors configured toexecute the executable instructions and cause the apparatus to: receivea characterization associated with a beam, wherein the characterizationcomprises array gain measurements or codebook beam characterizationmetrics; generate an embedding based on the characterization; providethe embedding to a wireless node, and train an autoencoder using afederated learning model, wherein to generate the embedding the one ormore processors are configured to cause the apparatus to generate theembedding using an encoder of the autoencoder.
 23. The apparatus ofclaim 22, wherein in causing the apparatus to provide the embedding, theone or more processors are configured to cause the apparatus to providea look-up table indicating the embedding associated with the beam. 24.The apparatus of claim 22, wherein: the characterization comprises aspherical array gain; the one or more processors are further configuredto cause the apparatus to convert the spherical array gain to a graph;and in causing the apparatus to generate the embedding, the one or moreprocessors are configured to cause the apparatus to generate theembedding using a graph convolution network based on the graph.
 25. Theapparatus of claim 22, wherein the one or more processors are furtherconfigured to cause the apparatus to receive one or more trainingcodebooks for training of the autoencoder.
 26. The apparatus of claim22, wherein the array gain measurements correspond to array gainmeasurements per beam on a sphere, and the codebook beamcharacterization metrics correspond to codebook beam characterizationmetrics per beam as an auxiliary input for the embedding, wherein incausing the apparatus to receive the characterization, the one or moreprocessors are configured to cause the apparatus to receive thecharacterization as a noise augmented input, and wherein in causing theapparatus to generate the embedding, the one or more processors areconfigured to cause the apparatus to generate the embedding using ade-noising auto-encoder based on the noise augmented input.
 27. Theapparatus of claim 22, wherein in causing the apparatus to generate theembedding, the one or more processors are configured to cause theapparatus to down sample the characterization using a Fourier transform.28. The apparatus of claim 22, wherein the characterization comprises aspherical array gain.